Label-free Characterization and Sorting
of Human Pathogens
C. Honrado1, J. McGrath2, L. Ciuffreda3, D. Spencer1, H. Bridle2, L. Ranford-Cartwright3, H. Morgan1
1Faculty
of Physical Sciences and Engineering, Institute for Life Sciences, University of Southampton, SO17 1BJ, Southampton, UK
2Institute of Biological Chemistry, Biophysics and Bioengineering, School of Engineering and Physical Sciences, Heriot-Watt University, EH14 4AS, Edinburgh, UK
3Institute of Infection, Immunity and Inflammation, College of Medical, Veterinary and Life Sciences, University of Glasgow, G12 8QQ, Glasgow, UK
Microfluidic Impedance Cytometry
b)
a)
AC Signal
t1
t2
t3
t4
t4
R
εmedium
εcytoplasm
σcytoplasm
σmembrane
c)
t3
σmedium
Cmembrane
Cmedium
+
-
t2
d
Rcytoplasm
t1
t0
Rmedium
t0
Differential Signal (V)
~
εmembrane
Ccytoplasm
Microfluidic Impedance Cytometry (MIC) measures
the dielectric properties of single cells as they pass
through a microchannel with integrated electrodes.
An electrical field is applied by the microelectrodes,
with the electrical impedance of single cells being
retrieved from their interaction with the field. A
measurable change in the differential signal,
obtained by the microelectrodes, is proportional to
particle impedance [1].
The application of numerous different frequencies
can be used as a cell-characterization tool, being
able to discriminate various cell types. Examples of
this discrimination are the differentiation of the three
main populations of white blood cells [2], stem cells
[3] or parasite-infected cells [4]. A high throughput
(up to 1000 cells per second) or the capability of
performing measurements using a physiological
medium are some of advantages of the system.
Cmembrane
Time (ms)
Figure 1 – a) Schematic representation of the MIC system structure and operation. Suspended cells are driven through the microchannel by a pressure driven flow. An AC
signal is applied on both top electrodes, generating an electric field. A differential signal between the measurements of the bottom electrodes is measured. After the cell
entrance in the channel (t0), it will flow until it reaches the electrode area (t1). At this point, given the usually insulating properties of polymer beads or cell membranes, the
signal in the first set of electrodes drop, resulting in a positive differential peak in the differential signal. The opposite occurs when the cell passes through the second set of
electrodes (t3), where a negative peak is observed in the differential signal. This differential signal is then used to identify cells and later perform the dielectric
characterization. b) Single-shell model used to model and characterize non-nucleated cells (such as red blood cells). Other models can be constructed to better represent the
cells/particles under study. c) Equivalent circuit model also used to characterize the cells under study.
Water-borne pathogens
b)
Cryptosporidium Parvum
7µm beads
Viable oocysts
1138 events
Non-viable oocysts
1602 events
Real part of signal at 18MHz
c)
Cryptosporidium Muris
Imaginary/Real part of signal at 18MHz
Imaginary/Real part of signal at 18MHz
a)
Imaginary/Real part of signal at 18MHz
From the different water-borne
pathogens, protozoan parasites, as
C.Parvum, C.Muris and G. Lamblia
still remain a problem, presenting
resistance to water treatment methods
and a low infectious dose [5].
Another remaining problem is related
to the differentiation of viable and
non-viable oocysts of the protozoan
parasite in the standard detection
method. An automated process could
reduce detection time, reduce the
level of human intervention required,
aid in better assessing the risk posed
to human health and contribute to the
saving of numerous resources.
Preliminary results show good
differentiation between viable and
non-viable samples.
7µm beads
Viable oocysts
610 events
Non-viable oocysts
677 events
Real part of signal at 18MHz
Giardia Lamblia
7µm beads
Viable oocysts
467 events
Non-viable oocysts
155 events
Real part of signal at 18MHz
Figure 2 – Scatter plots of the ratio between Imaginary and Real parts over the Real part of the signals measured by the MIC for different water-born pathogens a) Cryptosporidium Parvum, b)
Cryptosporidium Muris and c) Giardia Lamblia. Scatter plot represent two experiments, overlapped in the plot, performed at 18MHz, with samples containing polystyrene 7µm beads and
viable or non-viable oocysts of the pathogens in samples with different concentration of PBS: a) 4x, b) 1x and c) 0.25x.
Malaria Infected Red Blood Cells
Average Real and Imaginary Parts of Signal
Imaginary/Real part of signal at 18MHz
Human malaria is the world’s most a)
b)
Malaria
Frequency analysis
important
disease
caused
by
parasites. More than 40 % of the
world population lives where malaria
7µm beads
is endemic, where about 207 million
cases and 627 000 malaria deaths
Healthy RBCs
have been estimated in 2012 [6].
2249 events
A dielectric characterization of the
parasite could help develop a better
diagnostic tool, aimed at lowInfected RBCs (?)
concentration causative parasites.
55 events
Previous dielectric studies [7] showed
that the parasite’s presence causes
the cell to lose is ability to control its
Real part of signal at 18MHz
cytoplasmic ion concentration.
Frequency (Hz, Log10 scale)
Preliminary results are in accordance
Figure 3 – a) Scatter plot of the ratio between Imaginary and Real part over the Real part of one of the signals measured
with this notion. After a fitting
by the MIC for malaria infected red blood cells (iRBCs). Scatter plot represent a single experiment, performed at 18MHz,
process, the dielectric properties of
with sample containing polystyrene 7µm beads and populations of healthy RBCs (uRBCs) and iRBCs. b) Frequency
the healthy and infected red blood
response analysis of healthy and infected RBCs, with the average Real (blues, on top) and Imaginary (reds, on bottom)
parts of the measured signals for a frequency range of 250 kHz to 50 MHz. The error bars correspond to one standard
cells (iRBCs) can be retrieved. As
deviation, obtained from a set of 2249 ± 495 events/frequency, for uRBCs, and 55 ± 9 events/frequency, for iRBCs. A
expected, the cytoplasm conductivity
fitting process using the last mean squares method ensued, generating relaxation curves for the Real (greens, on top) and
of iRBCs matches that of the medium
Imaginary (darks, on bottom) parts, with known dielectric values. The results from this fitting method is presented in c),
where the permittivity, ε, and conductivity, σ, for the different cell components and medium can be found.
due to changes in the membrane.
References
[1] T. Sun, H. Morgan, “Single-cell microfluidic impedance cytometry: a review”, Microfluidics and Nanofluidics 8(4), 423-443 (2010).
[2] D. Holmes, D. Pettigrew, C. H. Reccius, J. D. Gwyer, C. V. Berkel, J. Holloway, D. E. Davie, and H. Morgan, “Leukocyte analysis and differentiation
using high speed microfluidic single cell impedance cytometry,” Lab Chip 9, 2881–2889 (2009).
[3] H. Song, Y. Wang, J. M. Rosano, B. Prabhakarpandian, C. Garson, K. Panta, and E. A. Lai, “A microfluidic impedance flow cytometer for identification
of differentiation state of stem cells,” Lab Chip 13, 2300–2310 (2013).
[4] C. Kuttel, E. Nascimento, N. Demierre, T. Silva, T. Braschler, P. H. Renaud, and A. G. Oliva, “Label-free detection of Babesia bovis infected red blood
cells using impedance spectroscopy on a microfabricated flow cytometer,” Acta Trop. 102, 63–68 (2007).
[5] H. Bridle, B. Miller, M. Desmulliez, “Application of microfluidics in waterborne pathogen monitoring: A review”, Water Research 55, 256-271 (2014).
[6] World Health Organization, “World Malaria Report” (2014).
[7] P. Gascoyne, C. Mahidol, M. Ruchirawat, J. Satayavivad, P. Watcharasit, F. Becker, “Microsample preparation by dielectrophoresis: isolation of
malaria”, Lab. Chip 2 (2), 70–75 (2002).
c)
εcytoplasm = 65
σcytoplasm = 0.65 S/m
εmembrane = 4
σmembrane = < 10-8 S/m
εcytoplasm = 68
σcytoplasm = 1.53 S/m
εcytoplasm = 65
σcytoplasm = 1.40 S/m
Uninfected RBC
Volume:105 ± 22 fL
(2.93 ± 0.21 µm)
Infected RBC Volume:
(3.18 ± 0.32 µm)
Parasite Volume: 38.9
fL (2.10 µm)
εmedium = 80
σmedium = 1.60 S/m
εmembrane = 4.8
σmembrane = 5.8 x 10-5 S/m
εmembrane = 7.2
σmembrane = < 10-8 S/m